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hahattpro / crowd_counting_framework (public) (License: Unspecified) (since 2020-12-23) (hash sha1)

backupfrom https://github.com/ttpro1995/crowd_counting_framework

Clone URLs: https://rocketgit.com/user/hahattpro/crowd_counting_framework ssh://rocketgit@ssh.rocketgit.com/user/hahattpro/crowd_counting_framework git://git.rocketgit.com/user/hahattpro/crowd_counting_framework

List of commits:

Subject	Hash	Author	Date (UTC)
some demo and playground code	b1d48dd89f1bf3a30b82c51b25cdd68fb77f1023	Thai Thien	2020-12-04 15:53:19
some predict demo	7f2214ad2882874d5407a2d4d5bd115b7089a020	Thai Thien	2020-12-02 14:55:22
demo	3d5fa0d764843d4fd780e44547fd0b13a367c6bc	Thai Thien	2020-11-27 19:28:37
notable image	b0f2b0e9732c2f2031831eb80eeaf7e6dab1f5bd	Thai Thien	2020-11-22 07:11:16
notebook	5b8b840bc1fd0d806a13646477988534f4982fac	Thai Thien	2020-11-22 07:10:42
l2_adamw2_bigtail13i_t13_sha	7fd22510845dec808100058190a202b805bf6a02	Thai Thien	2020-09-23 14:17:00
l2_adamw2_bigtail13i_t12_sha	bf67476db55f55f348068bc7683f68834659cc96	Thai Thien	2020-09-23 14:14:35
l2_adamw2_bigtail13i_t11_sha	1365b305bf541a3d941d173b169fa14aa811beb1	Thai Thien	2020-09-22 19:31:17
l2_adamw2_bigtail13i_t10_sha	4f6f2b8d3ac4654cc2ed94ca6900643f3ca07be9	Thai Thien	2020-09-22 19:27:24
l2_adamw2_bigtail13i_t9_sha	576b424688dff640a0a7d448cbd615cfb4f33d54	Thai Thien	2020-09-22 19:21:17
t8	9e00ee3a50c93bbd726ebd8554a68f535d836691	Thai Thien	2020-09-22 19:11:37
split a	a7b2115bee8ab76497528b84aee09c79631575b1	Thai Thien	2020-09-22 17:48:37
load_data_shanghaitech_256_v2 and l2_adamw2_bigtail13i_t7_sha	07bca1a460c30ed7ec7269d72dad99eef6fc96b3	Thai Thien	2020-09-22 17:04:45
adamw1_ccnnv7_t7_jhu	c5f106103aa3696827627f5e815105d9432e2acb	Thai Thien	2020-09-16 18:21:22
adamw1_ccnnv7_t6_jhu.sh	5c8644da7445b3dd4f6d2611d564592e8e8ed45c	Thai Thien	2020-09-16 18:19:07
adamw1_CompactCNNV7_t6_jhu	1fa1d544a254cd042bcdd36e8de2984fe3d5244f	Thai Thien	2020-09-16 17:56:15
adamw1_CompactCNNV7_t5_jhu	40720162c9e9b731fb852ed7e3e191228e421ced	Thai Thien	2020-09-16 17:46:20
test_data cache	73633cbd6d70448268a3e1534440601ddcf75276	Thai Thien	2020-09-16 17:12:57
fix test data not exist	474f506204251e26825801499f6bc503f44f8410	Thai Thien	2020-09-16 16:41:00
fix test data not exist	6377e18d87e0fabbf9b4ed143e26ae5912c0b872	Thai Thien	2020-09-16 16:40:06

Commit b1d48dd89f1bf3a30b82c51b25cdd68fb77f1023 - some demo and playground code
Author: Thai Thien
Author date (UTC): 2020-12-04 15:53
Committer name: Thai Thien
Committer date (UTC): 2020-12-04 15:53
Parent(s): 7f2214ad2882874d5407a2d4d5bd115b7089a020
Signing key:
Tree: 2653e96f8fe6a72c024d00583c02c1e7e4b1faeb

File	Lines added	Lines deleted
demo_app/predict_only.py	3	2
demo_app/predict_video.py	0	0
playground/cv_heatmap.py	40	0
playground/try_to_do_overlay.py	35	0
playground/video_with_torchvision.py	20	0

File demo_app/predict_only.py changed (mode: 100644) (index d677298..1702211)
...	...	if __name__ == "__main__":
10	10	output into another folder	output into another folder
11	11	output density map and count in csv	output density map and count in csv
12	12	"""	"""
13		NAME="bao2"
	13		NAME="for_question_forum"
14	14	# INPUT_FOLDER = "/data/ShanghaiTech/part_B/test_data/images/"	# INPUT_FOLDER = "/data/ShanghaiTech/part_B/test_data/images/"
15	15	INPUT_FOLDER = "/home/tt/Downloads/bao2"	INPUT_FOLDER = "/home/tt/Downloads/bao2"
16	16	OUTPUT_FOLDER = "/data/apps/tmp"	OUTPUT_FOLDER = "/data/apps/tmp"

...	...	if __name__ == "__main__":
21	21	model = DCCNN()	model = DCCNN()
22	22	model.load_state_dict(loaded_file['model'])	model.load_state_dict(loaded_file['model'])
23	23	model.eval()	model.eval()
24		os.mkdir(os.path.join(OUTPUT_FOLDER, NAME))
	24		os.makedirs(os.path.join(OUTPUT_FOLDER, NAME), exist_ok=True)
25	25	log_file = open(os.path.join(OUTPUT_FOLDER, NAME, NAME +".log"), 'w')	log_file = open(os.path.join(OUTPUT_FOLDER, NAME, NAME +".log"), 'w')
26	26	limit_count = 100	limit_count = 100
27	27	count = 0	count = 0

...	...	if __name__ == "__main__":
37	37	log_line = info["name"][0] + "," + str(pred_count.item()) +"\n"	log_line = info["name"][0] + "," + str(pred_count.item()) +"\n"
38	38	log_file.write(log_line)	log_file.write(log_line)
39	39	save_density_map(pred, predict_path)	save_density_map(pred, predict_path)
	40		torch.save(pred, predict_path+".torch")
40	41	print("save to ", predict_path)	print("save to ", predict_path)
41	42	count += 1	count += 1
42	43	log_file.close()	log_file.close()

File demo_app/predict_video.py copied from file debug/__init__.py (similarity 100%)

File playground/cv_heatmap.py added (mode: 100644) (index 0000000..dd2191b)
	1	import cv2
	2	import torch
	3	from torch import nn
	4	path = "/data/apps/tmp/for_question_forum"
	5	img_path = "/home/tt/Downloads/bao2/download.jpeg"
	6	density_map_path = "/data/apps/tmp/for_question_forum/PRED_download.jpeg.torch"
	7
	8	img_tensor = cv2.imread(img_path)
	9	print(type(img_tensor))
	10	print(img_tensor.shape)
	11
	12	density_map_tensor = torch.load(density_map_path)
	13	print(img_tensor.shape)
	14	print(density_map_tensor.shape)
	15	print(density_map_tensor.sum())
	16	density_map_tensor = torch.from_numpy(density_map_tensor).unsqueeze(dim=0).unsqueeze(dim=0)
	17	# module = nn.UpsamplingBilinear2d(scale_factor=8)
	18	# upsampling_density_map_tensor = module(density_map_tensor)
	19	upsampling_density_map_tensor = nn.functional.interpolate(density_map_tensor, scale_factor=8)/64
	20	print(upsampling_density_map_tensor.sum())
	21	print(upsampling_density_map_tensor.shape)
	22
	23	pad_density_map_tensor = torch.zeros((1, 3, img_tensor.shape[0], img_tensor.shape[1]))
	24	pad_density_map_tensor[:, 0,:upsampling_density_map_tensor.shape[2], :upsampling_density_map_tensor.shape[3]] = upsampling_density_map_tensor
	25	print(pad_density_map_tensor.shape)
	26	# pad_density_map_tensor = (pad_density_map_tensor.squeeze(dim=0)/pad_density_map_tensor.max()*255)
	27	pad_density_map_tensor = pad_density_map_tensor.squeeze(dim=0)/pad_density_map_tensor.max()
	28	print(pad_density_map_tensor.shape)
	29	pad_density_map_tensor_match = pad_density_map_tensor.permute(1,2,0)
	30	print(pad_density_map_tensor_match.shape)
	31	pad_density_map_tensor_match_np = pad_density_map_tensor_match.numpy()
	32	print(pad_density_map_tensor_match_np.shape)
	33	print(pad_density_map_tensor_match_np.dtype)
	34	pad_density_map_tensor_match_np = pad_density_map_tensor_match_np.astype("uint8")
	35	print(img_tensor.dtype)
	36	print(pad_density_map_tensor_match_np.dtype)
	37	print(pad_density_map_tensor_match_np[:,:,0:1].shape)
	38	overlay_color = cv2.applyColorMap(pad_density_map_tensor_match_np[:,:,0], colormap=cv2.COLORMAP_JET)
	39
	40	cv2.imwrite("../visualize/pic/cv2_overlay_color.png", overlay_color)

File playground/try_to_do_overlay.py added (mode: 100644) (index 0000000..187fcc6)
	1	from torchvision.io import write_png, read_image
	2	import torch
	3	from torch import nn
	4
	5	path = "/data/apps/tmp/for_question_forum"
	6	img_path = "/home/tt/Downloads/bao2/download.jpeg"
	7	density_map_path = "/data/apps/tmp/for_question_forum/PRED_download.jpeg.torch"
	8
	9	img_tensor = read_image(img_path)
	10	density_map_tensor = torch.load(density_map_path)
	11
	12	print(img_tensor.shape)
	13	print(density_map_tensor.shape)
	14	print(density_map_tensor.sum())
	15	density_map_tensor = torch.from_numpy(density_map_tensor).unsqueeze(dim=0).unsqueeze(dim=0)
	16	# module = nn.UpsamplingBilinear2d(scale_factor=8)
	17	# upsampling_density_map_tensor = module(density_map_tensor)
	18	upsampling_density_map_tensor = nn.functional.interpolate(density_map_tensor, scale_factor=8)/64
	19	print(upsampling_density_map_tensor.sum())
	20	print(upsampling_density_map_tensor.shape)
	21
	22	pad_density_map_tensor = torch.zeros((1, 3, img_tensor.shape[1], img_tensor.shape[2]))
	23	pad_density_map_tensor[:, 0,:upsampling_density_map_tensor.shape[2], :upsampling_density_map_tensor.shape[3]] = upsampling_density_map_tensor
	24	print(pad_density_map_tensor.shape)
	25	pad_density_map_tensor = (pad_density_map_tensor.squeeze(dim=0)/pad_density_map_tensor.max()*255)
	26	# pad_density_map_tensor = pad_density_map_tensor.squeeze(dim=0)
	27
	28	print(img_tensor.dtype)
	29	print(pad_density_map_tensor.dtype)
	30
	31	overlay_density_map = img_tensor.detach().clone()
	32	overlay_density_map[0] = torch.clamp_max(img_tensor[0] + pad_density_map_tensor[0] * 2, max=255)
	33
	34	write_png(overlay_density_map.type(torch.uint8), "../visualize/pic/overlay.png")
	35	write_png(pad_density_map_tensor.type(torch.uint8), "../visualize/pic/pad.png")

File playground/video_with_torchvision.py added (mode: 100644) (index 0000000..26db1f5)
	1	from torchvision.io.video import read_video, write_video
	2	from torchvision.io import write_png
	3	import os
	4
	5	video_path = "/home/tt/Videos/VID_20201202_133703_090.mp4"
	6	out_path = "../visualize/vid"
	7
	8
	9	def example_load_frame():
	10	v, a, info = read_video("/home/tt/Videos/VID_20201202_133703_090.mp4", pts_unit='sec')
	11	print(v.shape) # torch.Size([467, 1080, 1920, 3])
	12	# write a frame
	13	single_frame = v[100]
	14	print(single_frame.shape) # torch.Size([1080, 1920, 3])
	15	single_frame = single_frame.permute(2,0,1) # to CHW
	16	print(single_frame.shape)
	17	file_out = os.path.join(out_path, "single_frame.png")
	18	write_png(single_frame, file_out)
	19	print("done write to ", file_out)
	20

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